My Blog

Author: saqibkhan

  • The R Journal

    • Website: The R Journal
    • Overview: The official journal of the R project, featuring peer-reviewed articles on R programming, methodologies, and applications. It’s an excellent resource for in-depth research and discussions on statistical methods.

  • Blog.rstudio.com – R Markdown

    • Website: R Markdown Blog
    • Overview: This section of the RStudio blog focuses specifically on RMarkdown, providing tutorials and updates on features that help users create dynamic documents.

  • Nate Silver’s FiveThirtyEight

    • Website: FiveThirtyEight
    • Overview: While primarily a data journalism site, FiveThirtyEight frequently uses R for its analyses. The blog often provides insights into data visualization and statistical modeling.

  • Revolutions

    • Website: Revolutions
    • Overview: Maintained by Microsoft, this blog discusses R and data science topics, often featuring case studies, new R packages, and industry applications of R.

  • The R Graph Gallery

    • Website: The R Graph Gallery
    • Overview: Focused on data visualization with R, this site features numerous examples and tutorials for creating different types of plots using ggplot2 and other visualization packages.

  • RStudio Cheatsheets

    • Website: RStudio Cheatsheets
    • Overview: While not a blog, this resource provides concise guides on using various R packages and functions. They are excellent for quick reference and learning.

  • The Analysis Factor

    • Website: The Analysis Factor
    • Overview: Focuses on statistical analysis using R and other tools. The blog provides practical advice, tutorials, and webinars aimed at researchers and data analysts.

  • Data Science Central

    • Website: Data Science Central
    • Overview: While not exclusively about R, it features many articles and tutorials on data science topics, including those that use R for analysis and visualization.

  • RStudio Blog

    • Website: RStudio Blog
    • Overview: The official blog of RStudio, it provides updates about new features, tutorials, and tips for using R and RStudio. It also covers best practices for using the tidyverse and RMarkdown.

  • What is the difference between the with() and within() functions?

    The with() function evaluates an R expression on one or more variables of a data frame and outputs the result without modifying the data frame. The within() function evaluates an R expression on one or more variables of a data frame, modifies the data frame, and outputs the result. Below we can see how these functions work using a sample data frame as an example:

    df <- data.frame(a = c(1, 2, 3),  b = c(10, 20, 30))
print(df)

with(df, a * b)

print(within(df, c <- a * b))

    Output:

      a  b
1 1 10
2 2 20
3 3 30

10  40  90
  a  b  c
1 1 10 10
2 2 20 40
3 3 30 90